AI/ML Engineer

About the Role CODVO.AI is looking for a Senior AI/ML Engineer to join our Cloud Apps & Modernization team. The ideal candidate will have strong hands-on experience in Python, Machine Learning, backend engineering, cloud platforms, and production-grade ML systems. You will be responsible for designing, developing, deploying, and optimizing AI/ML solutions that integrate with modern cloud applications. The role requires a strong combination of ML expertise and software engineering skills, with the ability to take solutions from experimentation and prototyping through production deployment. You will also contribute to technical architecture, mentor junior engineers, and work closely with Data Scientists, Cloud Engineers, Product Managers, and other stakeholders. Key Responsibilities Machine Learning Engineering Design and develop end-to-end machine learning solutions using Python. Build ML pipelines covering data preparation, feature engineering, model training, evaluation, deployment, and monitoring. Develop and optimize supervised and unsupervised machine learning models. Perform model evaluation, hyperparameter tuning, experimentation, and performance optimization. Analyze model performance and identify opportunities for improving accuracy, scalability, and reliability. Convert experimental/prototype ML solutions into production-ready systems. Backend & Cloud Engineering Design and develop scalable backend services that integrate AI/ML capabilities into cloud applications. Build REST APIs and microservices for ML model serving and application integration. Deploy and maintain ML applications across AWS, Azure, or GCP environments. Design solutions for scalability, reliability, security, and performance. Troubleshoot production issues and optimize inference latency, throughput, and resource utilization. MLOps & Productionization Implement ML lifecycle best practices including experiment tracking, model versioning, deployment, monitoring, and rollback. Build and maintain CI/CD pipelines for ML applications and workflows. Implement automated testing and validation for ML models and supporting services. Work with tools such as MLflow, Kubeflow, DVC, or equivalent MLOps platforms. Establish practices for model monitoring, data drift detection, and performance tracking. Technical Leadership Participate in architecture and technical design discussions. Provide technical guidance and mentorship to junior and mid-level engineers. Conduct code reviews and promote engineering best practices. Evaluate emerging AI/ML technologies and recommend appropriate tools and frameworks. Collaborate with cross-functional teams to translate business requirements into scalable technical solutions. Communicate technical concepts and trade-offs effectively to both technical and non-technical stakeholders. Required Skills & Qualifications 6+ years of professional experience in software engineering, backend development, machine learning engineering, or a closely related field. Strong hands-on experience with Python for production applications and ML solutions. Strong understanding of machine learning fundamentals, including: Supervised and unsupervised learning Feature engineering Model evaluation and validation Hyperparameter tuning Model optimization Hands-on experience with ML frameworks such as Scikit-learn, PyTorch, TensorFlow, or equivalent. Proven experience building and deploying production-grade ML solutions. Experience deploying ML applications on at least one major cloud platform: AWS Azure GCP Strong understanding of software engineering fundamentals including: Object-oriented programming Design patterns Data structures and algorithms Unit/integration testing Git/version control Code quality and documentation Experience designing and developing REST APIs and backend services. Good understanding of database concepts and data processing. Strong debugging and problem-solving skills. Ability to independently own technical problems from requirements through implementation and production deployment. Strong communication and collaboration skills. Good to Have Hands-on experience with Docker and Kubernetes. Experience with MLOps tools such as MLflow, Kubeflow, DVC, Airflow, or equivalent. Experience with distributed computing frameworks such as Apache Spark or Dask. Experience designing microservices architectures. Experience with data engineering technologies such as Kafka, Airflow, or similar tools. Experience in NLP, Generative AI, LLMs, Computer Vision, or other advanced AI technologies. Experience with model serving frameworks and scalable inference architectures. Understanding of cloud-native architecture and serverless technologies. Contributions to open-source projects, technical publications, or ML research.